package ds_industry_2025.ds.ds_07.T3

import org.apache.spark.sql.SparkSession

/*
    2、根据dwd层表统计每个省每月下单的数量和下单的总金额，并按照year，month，region_id进行分组,按照total_amount降序排序，
    形成sequence值，将计算结果存入Hive的dws数据库的province_consumption_day_aggr表中（表结构如下），然后使用hive cli根据
    订单总数、订单总金额、省份表主键均为降序排序，查询出前5条，在查询时对于订单总金额字段将其转为bigint类型（避免用科学计数法展示
    ），将SQL语句复制粘贴至客户端桌面【Release\任务B提交结果.docx】中对应的任务序号下，将执行结果截图粘贴至客户端桌
    面【Release\任务B提交结果.docx】中对应的任务序号下;
 */
object t5 {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("T2")
      .config("hive.exec.dynamic.partition.mode","nonstrict")
      .config("spark.serializer","org.apache.spark.serializer.KryoSerializer")
      .config("spark.sql.extensions","org.apache.spark.sql.hudi.HoodieSparkSessionExtension")
      .enableHiveSupport()
      .getOrCreate()

    spark.table("dwd.dim_region")
      .where("etl_date=(select max(etl_date) from dwd.dim_region)")
      .createOrReplaceTempView("r")

    spark.table("dwd.dim_province")
      .where("etl_date=(select max(etl_date) from dwd.dim_province)")
      .createOrReplaceTempView("p")

    spark.table("dwd.fact_order_info")
      .createOrReplaceTempView("o")

    val result = spark.sql(
      """
        |select distinct
        |province_id,province_name,
        |region_id,region_name,
        |total_amount,total_count,
        |row_number() over(partition by year,month,region_id,region_name order by total_amount desc ) as sequence,
        |year,month
        |from(
        |select distinct
        |o.province_id,
        |p.name as province_name,
        |p.region_id,
        |r.region_name,
        |sum(o.final_total_amount)
        |over(partition by year(o.create_time),month(o.create_time),p.region_id,r.region_name,o.province_id,p.name) as total_amount,
        |count(*)
        |over(partition by year(o.create_time),month(o.create_time),p.region_id,r.region_name,o.province_id,p.name) as total_count,
        |year(o.create_time) as year,
        |month(o.create_time) as month
        |from o
        |join p on p.id=o.province_id
        |join r on r.id=p.region_id
        |) as r1
        |""".stripMargin)

    result.show

    spark.close()
  }

}
